In this study we explore statistical methods for optimizing an experimental design to distinguish between competing models. Information about model performance and the experimental design are integrated to identify variable settings that will maximally discriminate the models. The problem of design optimization is challenging because of the many, sometimes arbitrary, choices that must be made when designing an experiment. Nevertheless, it is generally possible to find a design that is optimal in a defined sense. For example, in designing an experiment that investigates retention, the experimenter must choose the number of time intervals between the study and test sessions and the actual time values when memory is probed. Design optimization methods provide a framework for exploiting this information for the purpose of improving model discrimination. In this talk, we will review various Monte Carlo approaches to design optimization that have been proposed in the literature, and present preliminary results from our own applications of such approaches to discriminating between retention, and others models of cognition, if time permits. |